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16
AllDifferentbased Filtering for Subgraph Isomorphism
"... The subgraph isomorphism problem involves deciding if there exists a copy of a pattern graph in a target graph. This problem may be solved by a complete tree search combined with filtering techniques that aim at pruning branches that do not contain solutions. We introduce a new filtering algorithm b ..."
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The subgraph isomorphism problem involves deciding if there exists a copy of a pattern graph in a target graph. This problem may be solved by a complete tree search combined with filtering techniques that aim at pruning branches that do not contain solutions. We introduce a new filtering algorithm based on local all different constraints. We show that this filtering is stronger than other existing filterings — i.e., it prunes more branches — and that it is also more efficient —i.e., it allows one to solve more instances quicker.
RealTime Exact Graph Matching with Application in Human Action Recognition
"... Abstract. Graph matching is one of the principal methods to formulate the correspondence between two set of points in computer vision and pattern recognition. However, most formulations are based on the minimization of a difficult energy function which is known to be NPhard. Traditional methods sol ..."
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Abstract. Graph matching is one of the principal methods to formulate the correspondence between two set of points in computer vision and pattern recognition. However, most formulations are based on the minimization of a difficult energy function which is known to be NPhard. Traditional methods solve the minimization problem approximately. In this paper, we show that an efficient solution can be obtained by exactly solving an approximated problem instead of approximately solving the original problem. We derive an exact minimization algorithm and successfully applied to action recognition in videos. In this context, we take advantage of special properties of the time domain, in particular causality and the linear order of time, and propose a novel spatiotemporal graphical structure. Keywords: Spacetime graph, Hypergraph matching, Action recognition 1
Recognizing and localizing individual activities through graph matching
, 2010
"... In this paper we tackle the problem of detecting individual human actions in video sequences. While the most successful methods are based on local features, which proved that they can deal with changes in background, scale and illumination, most existing methods have two main shortcomings: first, th ..."
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In this paper we tackle the problem of detecting individual human actions in video sequences. While the most successful methods are based on local features, which proved that they can deal with changes in background, scale and illumination, most existing methods have two main shortcomings: first, they are mainly based on the individual power of spatiotemporal interest points (STIP), and therefore ignore the spatiotemporal relationships between them. Second, these methods mainly focus on direct classification techniques to classify the human activities, as opposed to detection and localization. In order to overcome these limitations, we propose a new approach, which is based on a graph matching algorithm for activity recognition. In contrast to most previous methods which classify entire video sequences, we design a video matching method from two sets of STpoints for human activity recognition. First, points are extracted, and a hyper graphs are constructed from them, i.e. graphs with edges involving more than 2 nodes (3 in our case). The activity recognition problem is then transformed into a problem of finding instances of model graphs in the scene graph. By matching local features instead of classifying entire sequences, our method is able to detect multiple different activities which occur simultaneously in a video sequence. Experiments on two standard datasets demonstrate that our method is comparable to the existing techniques on classification, and that it can, additionally, detect and localize activities.
Fast exact matching and correspondence with hypergraphs on spatiotemporal data
, 2012
"... Graphs and hypergraphs are frequently used to recognize complex and often nonrigid patterns in computer vision, either through graph matching or pointset matching with graphs. Most formulations resort to the minimization of a difficult energy function containing geometric or structural terms, fre ..."
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Cited by 4 (3 self)
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Graphs and hypergraphs are frequently used to recognize complex and often nonrigid patterns in computer vision, either through graph matching or pointset matching with graphs. Most formulations resort to the minimization of a difficult energy function containing geometric or structural terms, frequently coupled with data attached terms involving appearance information. Traditional methods solve the minimization problem approximately, for instance with spectral techniques. In this paper we deal with data embedded in a 3D ”spacetime”, for instance in action recognition applications. We show that, in this context, we can take advantage of special properties of the time domain, in particular causality and the linear order of time. We show that the complexity of the exact matching problem is far inferior to the complexity of the general problem and we derive an algorithm calculating the exact solution. As a second contribution, we propose a new graphical structure which is elongated in time. We argue that, instead of approximately solving the original problem, a better solution can be obtained by exactly solving an approximated problem. An exact minimization algorithm is derived for this structure and successfully applied to action recognition in videos.
A computational study of problems in sports
, 2010
"... c ○ Tyrel Clinton Russell 2010I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. This thesis examines three ..."
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c ○ Tyrel Clinton Russell 2010I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. This thesis examines three computational problems in sports. The first problem addressed is determining the minimum number of points needed to guarantee qualification for the playoffs and the minimum number of points needed to have a possibility of qualification for the playoffs of the National Hockey League (NHL). The problem is solved using a phased approach that incrementally adds more complicated tiebreaking constraints if a solution is not found. Each of the phases is solved using a combination of network flows, enumeration and constraint programming. The experimental results show that the solver efficiently solves instances at any point of the season. The second problem addressed is determining the complexity, either worstcase theoretical or practical, of manipulation strategies in sports tournaments. The two most common types of competitions, cups and round robins, are considered and it is shown that there exists a number of polynomial time algorithms
CP models for maximum common subgraph problems
 In Proceedings of CP’11
, 2011
"... Abstract. The distance between two graphs is usually defined by means of the size of a largest common subgraph. This common subgraph may be an induced subgraph, obtained by removing nodes, or a partial subgraph, obtained by removing arcs and nodes. In this paper, we introduce two soft CSPs which mod ..."
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Abstract. The distance between two graphs is usually defined by means of the size of a largest common subgraph. This common subgraph may be an induced subgraph, obtained by removing nodes, or a partial subgraph, obtained by removing arcs and nodes. In this paper, we introduce two soft CSPs which model these two maximum common subgraph problems in a unified framework. We also introduce and compare different CP models, corresponding to different levels of constraint propagation. 1
An empirical study of seeding manipulations and their prevention
 in Proc. of 22nd IJCAI
"... It is well known that cheating occurs in sports. In cup competitions, a common type of sports competition, one method of cheating is in manipulating the seeding to unfairly advantage a particular team. Previous empirical and theoretical studies of seeding manipulation have focused on competitions wi ..."
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Cited by 2 (1 self)
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It is well known that cheating occurs in sports. In cup competitions, a common type of sports competition, one method of cheating is in manipulating the seeding to unfairly advantage a particular team. Previous empirical and theoretical studies of seeding manipulation have focused on competitions with unrestricted seeding. However, real cup competitions often place restrictions on seedings to ensure fairness, wide geographic interest, and so on. In this paper, we perform an extensive empirical study of seeding manipulation under comprehensive and realistic sets of restrictions. A generalized random model of competition problems is proposed. This model creates a realistic range of problem instances that are used to identify the sets of seeding restrictions that are hard to manipulate in practice. We end with a discussion of the implications of this work and recommendations for organizing competitions so as to prevent or reduce the opportunities for manipulating the seeding. 1
Y.: Topological features and iterative node elimination for speeding up subgraph isomorphism detection
 In: Proceedings of the 21st International Conference on Pattern Recognition
, 2012
"... In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algor ..."
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In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algorithm, both current and future. These enhancements consist of a number of topological features to be added to the nodes, and a technique which we term “iterative node elimination”. The fusion of these enhancements is shown to be able to reduce subgraph isomorphism matching times by a factor of over 4,500. 1
ClouDiA: A Deployment Advisor for Public Clouds
"... An increasing number of distributed datadriven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate vi ..."
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An increasing number of distributed datadriven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances noncontiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixedinteger programming and constraint programming techniques to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that our techniques yield a 15 % to 55 % reduction in timetosolution or service response time, without any need for modifying application code. 1.
A unified framework for strengthening topological node features and its application to subgraph isomorphism N. Dahm et al
 Pattern Recognition 48 (2015) 317–330 329 detection, in: W.G. Kropatsch, N.M. Artner, Y. Haxhimusa, X. Jiang (Eds.), GraphBased Representations in Pattern Recognition, Lecture Notes in Computer Science, 7877
, 2013
"... Abstract. This paper presents techniques to address the complexity problem of subgraph isomorphism detection on large graphs. To overcome the inherently high computational complexity, the problem is simplified through the calculation and strengthening of topological node features. These features can ..."
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Abstract. This paper presents techniques to address the complexity problem of subgraph isomorphism detection on large graphs. To overcome the inherently high computational complexity, the problem is simplified through the calculation and strengthening of topological node features. These features can be utilised, in principle, by any subgraph isomorphism algorithm. The design and capabilities of the proposed unified strengthening framework are discussed in detail. Additionally, the concept of an nneighbourhood is introduced, which facilitates the development of novel features and provides an additional platform for feature strengthening. Through experiments performed with stateoftheart subgraph isomorphism algorithms, the theoretical and practical advantages of using these techniques become evident.